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Mengran Li

PhD Candidate in Statistics School of Mathematics and Statistics, University of Glasgow
Extreme value theory | Causal inference

I am a graduating Ph.D. researcher specializing in the intersection of Extreme Value Theory (EVT) and Causal Inference, with a focus on applications in Climate Extremes and Environmental Statistics.

I am currently exploring my next career steps and actively seeking Lectureship, research-intensive, or Postdoctoral positions (including competitive fellowships like EPSRC/Leverhulme). I welcome contact from supervisors or institutions interested in hosting my research.

My full CV is attached for your review.
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Education

Doctor of Philosophy in Statistics

University of Glasgow · Glasgow, UK
2022 – Present
PhD in Statistics under the supervision of Dr. Daniela Castro-Camilo.

Master of Science in Statistics (with Distinction)

University of Glasgow · Glasgow, UK
2020 – 2021
Dissertation: Spatiotemporal modelling and prediction of average flows in Scotland based on generalized additive models.

Bachelor of Science in Statistics (Major) and Finance (Double Major)

2015 – 2019
Completed Bachelor’s degrees with a strong foundation in probability, statistical inference, and mathematical statistics.

Research

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Causal Discovery in Extremes

 Developing novel causal inference algorithms tailored for extreme value regimes, currently focusing on theoretical robustness and validation using extensive simulation studies.
Oct 1, 2025
1 min read

Extreme quantile treatment effect (EQTE)

 Developing novel methods for estimating treatment effects at the extremes of an outcome distribution, with the manuscript currently under preparation for journal submission.
Oct 1, 2025
1 min read

Extreme Event Attribution (EEA)

 Exploring how tail assumptions influence attribution of extreme climate events, combining simulation, spatial methodology, and software development.
Jul 1, 2025
4 min read

Wee Extremes: EVA 2023 Data Challenge

 Statistical Methodology for Extreme Event Risk Assessment in the EVA 2023 Data Challenge.
May 30, 2023
2 min read